Writer Identification Using Texture Analysis

In this paper, we describe a new method to identify the writer of handwritten documents. There are many methods for signature verification or writer identification, but most of them require segmentation or connected component analysis. They are the kinds of content dependent identification methods as signature verification requires the writer to write the same text (e.g. his name). In our new method, we take the handwriting as an image containing some special texture, and writer identification is regarded as texture identification. This is a content independent for a particular script. We apply the well-established 2-D Gabor filtering technique and grey scale co-occurrence matrices to extract features of such textures and a weighted Euclidean distance classifier and K-nearest neighbor classifier to fulfill the identification task. Experiments are made using handwritings from different people in different languages and very promising results were achieved.